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Related Experiment Video

Updated: Jun 8, 2026

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

Published on: April 11, 2025

Learning to use an invisible visual signal for perception.

Massimiliano Di Luca¹, Marc O Ernst, Benjamin T Backus

¹Max Planck Institute for Biological Cybernetics, Tübingen, Germany. max@tuebingen.mpg.de

Current Biology : CB

|October 12, 2010

Summary

The brain can automatically learn new associations between invisible visual cues and perceptual outcomes. This structure learning happens without conscious awareness, demonstrating rapid, low-level perceptual adaptation.

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Published on: May 23, 2019

Area of Science:

Neuroscience
Cognitive Science
Visual Perception

Background:

Perceptual learning involves adapting to statistical regularities in sensory input.
Previous studies demonstrated learning from visible signals, but automaticity remained unclear.
Investigating low-level, automatic learning requires stimuli with invisible perceptual consequences.

Purpose of the Study:

To determine if perceptual learning, causing new appearance effects, can be low-level and automatic.
To examine structure learning of novel contingencies between sensory signals.
To assess if the brain automatically recruits signals for new perceptual uses.

Main Methods:

Used a rotating cylinder stimulus with an invisible vertical disparity gradient.
Masked the target signal with other depth cues to exclude high-level influences like attention.
Introduced a novel contingency between the invisible signal and cylinder rotation direction during exposure.
Tested perception using an ambiguously rotating cylinder after exposure.

Main Results:

The invisible signal significantly influenced the perceived rotation direction of the cylinder.
Demonstrated that structure learning can occur even when perceptual consequences are not consciously perceived.
Showed automatic recruitment of sensory signals for novel perceptual functions.

Conclusions:

Perception rapidly undergoes structure learning by automatically detecting novel signal contingencies.
This automatic learning process operates at a low level, independent of attention or consciousness.
Sensory signals can be automatically repurposed for new uses in constructing percepts.